Neither models nor miracles: a look at synthetic biology

Ars takes an in-depth look at the field of synthetic biology—the computer …

The 20th century broke open both the atom and the human genome. Physics deftly imposed mathematical order on the upwelling of particles. Now, in the 21st century, systems biology aims to fit equations to living matter, creating mathematical models that promise new insight into disease and cures. But, after a decade of effort and growth in computing power, models of cells and organs remain crude. Researchers are retreating from complexity towards simpler systems. And, perversely, ever-expanding data are making models more complicated instead of accurate. To an extent, systems biology, rather than climbing upwards to sparkling mathematical vistas, is stuck in a mire of its own deepening details.

Synthetic biology does away with systems biology's untidiness by focusing on individual parts, creating a tool set for engineering organisms unconstrained by biology as we know it, making the discipline more like software programming. But instead of modularity, synthetic biology often encounters messiness. What a particular “part” actually does depends on the rest of the system, so synthetic biology rediscovers the complexity it hoped to escape.

A dream deferred

Systems biology takes over where the sequencing of the human genome left off. Reading DNA, we once believed, would disclose the genes underlying disease. While hundreds of genes have been implicated in cancer, for instance, the idea that genes directly cause complex diseases like cancer, diabetes, or atherosclerosis is too simple. Systems biology seeks to understand how biology really works by looking beyond genes, aspiring to a universal understanding of biological organization and to models that precisely predict biological events—like disease.

Focused use of systems biology has been very successful, drawing information out of the huge bodies of data produced by DNA chips and proteomic gels. But our growing genomic knowledge and powerful computers have tempted a number of researchers to try to skip the analysis of data produced using biology, and attempt to produce a realistic cell entirely in silico.

These efforts began with a cell related to those that make up humans, but much simpler: yeast. The first yeast model appeared in 2001, created by the Institute for Systems Biology. In 2002, the Alpha Project began its trek toward omega, with yeast also as the first step. 2004 saw the unveiling of another model, YeastNet, which researchers expected would become more and more accurate "simply by continued addition of functional genomics data..."

Genomics data cooperated, propagating with exponential fervor. Data on proteins obligingly followed a similar path, according to a Moore’s law for proteomics. Consequently, a 2007 update to YeastNet encompassed 82 percent of yeast genes and more than 95 percent of the yeast proteome. But it showed no real gain in accuracy.

Edward Marcotte, who leads the effort at the University of Texas, said in March that his team had updated YeastNet. He claimed to see "a nice increase in predictive power over v. 2," however he has "yet to write it up or release it," suggesting that, even with improvements, the results might not be so prepossessing. YeastNet’s publication arc has tailed down, beginning from the commanding heights of Science, descending in version two to the egalitarian plain of PLos ONE and now apparently a file drawer at UT Austin. Similar difficulties beset the Alpha Project, which repeatedly scaled back its ambitions until it winked out of existence in 2008.

Premonitions of yeast’s unruliness came sooner in some quarters. In 2004, the Institute for Systems Biology turned to a much simpler species it believed to be "better suited for the initial phase" of systems biology. The new organism, a bacteria called H. salinarum, had just 2,400 genes, compared to over 6,000 in yeast. A group at the European Molecular Biology Lab switched in 2009 to the even smaller M. pneumoniae, which has a mere 687 genes. As "-omics" data grew, the complexity of organisms modeled by systems biology dropped.

Running ≠ Hiding

But retreat from complexity is not escape. Looking at M. pneumoniae, scientists concluded that "there is no such a thing as a 'simple' bacterium." The tiny organism's modest genetic machinery proved baffling, causing a mismatch between what the genomic model predicted and the proteomic reality. Mum about its own inner workings, neither did M. pneumoniae disclose any fundamental principles of biology which researchers said "remain elusive "

The outpouring of data has failed to coalesce into a solid theoretical foundation from which to build. As Anne-Claude Gavin, senior author on the M. pneumoniae paper, said: "I believe what we still miss in the majority of the cases is a structuring frame on which to integrate or superimpose the large datasets gathered..."

Of course the search continues, but each iteration adds to the difficulties. For example, to better understand the swirling protein complexes seen through the window of proteomics, we could zoom in and supply any number of biophysical details, like how quickly interactions take place, for instance. But this adds layers to the model. And more complicated models are less likely to work or to reveal simple, elegant principles of biology.

Yet the cycle almost necessarily continues. As two researchers put it in the pages of Nature: "The inescapable reality in systems biology is that models will continue to grow in size, complexity, and scope."

A growth industry

There’s not really an upper limit on model size, either. Because living organisms progress through time which can be sliced to arbitrary thinness, data space is effectively infinite. Time presents serious difficulties for systems biology. To create a virtual physiologic human, the discipline would need to span 17 or more orders of magnitude, from the nanoseconds of molecular motion all the way up to the years and decades of human life spans.

The number of spatial scales too is daunting, from nanometers to meters, at least nine orders of magnitude. And as we’ve studied this enormous biological time-space in more detail, it’s produced a profusion of discoveries, the many new kinds of RNA, for instance. Estimates for the total number of molecular species in a human cell range as high as one million.

"There are so many unknowns that it seems we are condemned to spend many years collecting data before we can even start to think about modelling what is going on," as Mike Williamson at the University of Sheffield put it. In the meantime, concluded Williamson, "it is only reasonable to expect that the model can predict something that it was designed to predict "

That’s a rather large concession. Lee Hood, whose lab at Caltech invented the DNA sequencer, once envisioned predicting the behavior of a system "given any perturbation. Not just the ones you’ve seen before, which we’re really good at, right? But any perturbation." That was in 2003, shortly after Hood founded the Institute for Systems Biology.

46 Reader Comments

Right now - if you're an adherent of the technological singularity, you should go back and read this article again. Gape in awe at the sheer overwhelming complexity of even the most humble living organism. The harder we look, the more life baffles us. As Francis Bacon said, "Nature is the art of God".

These science articles are really insightful - I am a "systems biologist" myself. I however think that despite all the messiness associated with systems biology we need to persevere. Even if it is not possible to avoid the messiness one can still derive insights that can help transform medicine and biology in general. I wonder whether one should expect there to be at least some simplifying principles in biology solely based on the fact that they are evolved systems - it seems to me that there is some limit as to the complexity that such a process can support (this is not a creationist argument by any chance - I take natural processes as given and try to reason from there). Maybe Stuart Kauffman's attractors would be a way.

But maybe you can do an article on cancer systems biology or something at another time. There are fascinating aspects to it both in the open source software development side, data visualization (this is really becoming a problem with more and more data types being added) as well as of course statistics and mathematical modeling.

Having worked in synthetic biology for a few years back in the mid-2000's, I wholeheartedly support this article; you have no idea the complexity involved in getting just a few genes working together to accomplish the task you design for them. There will always be unintended interactions, and if you're asking too much of the cell it'll just mutate away your changes anyway. In my opinion, evolutionary approaches to forcing cells to do your bidding is the only viable way forward, but that gives no insight into how they're doing what they're doing...I don't have an answer, but at least this article is asking the right questions.

Oh, and your next in-depth biology article should be on evo-devo...you guys think this stuff is complex, just wait until you see the networks they're developing...

I can relate to the scale of the problem biologists face. I work in the field of building modelling/simulation, which in some respects is vastly simpler but raises the same issues with complexity. Although the problems seem trivial (or even non-existent) at an intuitive level, they defy all attempts at rational analysis. This creates conflict between those who have really grappled with this type of problem and those who work with problems that can be encapsulated in tidy mathematical expressions.

I first met this conflict when I briefly worked with a firm designing electronic circuitry. The behaviour of the circuit could be described and simulated in very concrete, rational terms. Problems could be solved by relatively tiny algorithms that simultaneously weighed up all combinations and permutations of possible design solutions (although sometimes consuming huge amounts of RAM). Those who had worked in this field all their lives could not conceive of a problem that couldn't be reduced to an algorithm. It strikes me that this kind of thinking started biologists down this track, and I'm beginning to believe the attempt is misguided. This article confirms my concerns.

I took it for cynicism, but perhaps there's some truth in the old saying, "No one can comprehend what goes on under the sun. Despite all his efforts to search it out, man cannot discover its meaning. Even if a wise man claims he knows, he cannot really comprehend it."

It's like I always said: biology is just like code. The problem with the analogy people normally take with code is they think of biology as GOOD code. Code that is modular (not spaghetti), readable (has good names) and basically understandable (simple elegant logic). But what this analogy misses is that the more stupid and incompetent the programmer the less these things hold until at one point it is no longer worth it to fix bugs in code instead of scrapping it altogether and starting from scratch. The thing with biology is that the programmer is natural selection. A blind agent with absolutely no intelligence whatsoever. Any code written by natural selection, almost by definition, has to be horrible. And this is exactly what we're finding.

It's like I always said: biology is just like code. The problem with the analogy people normally take with code is they think of biology as GOOD code. Code that is modular (not spaghetti), readable (has good names) and basically understandable (simple elegant logic). But what this analogy misses is that the more stupid and incompetent the programmer the less these things hold until at one point it is no longer worth it to fix bugs in code instead of scrapping it altogether and starting from scratch. The thing with biology is that the programmer is natural selection. A blind agent with absolutely no intelligence whatsoever. Any code written by natural selection, almost by definition, has to be horrible. And this is exactly what we're finding.

I really like this interpretation. You could even go crazier and say it's like we are taking already compiled binaries and trying to extract out its subparts and divine all possible interactions. The catch is we have no model of the machine which executes that code, and it has taken decades to create systems that, with much effort, can read and write the code.

SO if DNA was supposed to encode the information that is the organism but what it encodes is largely contextual (the context being in part the organism itself) then where is the information describing the organism really stored? Some sort of bizarre epigenetic feedback?

One approach that will produce major results will be based on Craig Venter's system. First his group has to complete the task of developing the technology needed to use the system for routine experimentation. It is more or less the simplest system that can make it on its own in the wild. Once it is possibly to easily modify its parts in predictable ways, there will be a systematic effort to break those parts down further into subsystems. There will also be an effort to add functions. Experiments based on the ability to modify the system at will, to do various kind of measurements of dynamic properties, and to do mathematical modelling of the system will produce real results. Once the work with the system builds up to a basic foundation level of understanding a Moore's law phenomenon is likely to emerge. That is peopel will steadily be able to understand more complex aspects of the system and engineer variants of it that work in predictable ways. Eventually that ability will progress to the level needed to engineer something on the scale of a full function bacterium. Later it will progress to the level required to engineer something on the level of compexity of an animal cell. Until it gets going, it is hard to predict how long the process will take. But once a large number of people can work routinely with a relatively well understood foundation, progress could be quite rapid.

SO if DNA was supposed to encode the information that is the organism but what it encodes is largely contextual (the context being in part the organism itself) then where is the information describing the organism really stored? Some sort of bizarre epigenetic feedback?

I would agree with this. Makes sense insofar as evolution isn't an interpreter - if you want to encode yourself in DNA, you'll need to provide your own mechanism for reading & enacting it. There is no "DNA.exe" akin to "perl.exe" or "ruby.exe"....

"bizarre epigenetic feedback" - nice phrase. In the epically titled "The Origin of Consciousness in the Breakdown of the Bicameral Mind," Julian Jaynes posits that consciousness itself arises from (or, "is") a similarly looped construct.

A blind agent with absolutely no intelligence whatsoever. Any code written by natural selection, almost by definition, has to be horrible. And this is exactly what we're finding.

I don't necessarily buy that.

It's horrible from the perspective of a rational agent trying to comprehend/change it. But from a functional perspective? It's quite elegant in places.

Biology wastes very little; it can't afford to, what with all that competition out there. Human beings have fairly few active genes, but the amino acid sequences used by these genes are used in such a staggering diversity of places and ways that we don't need a lot of genes.

Indeed, I think of it not as the stereotypical bad code, where a team has been hacking away at a codebase for decades, with the original developers long since removed, and nobody knows how it works anymore. Instead, I think of it more as the pinnacle of hyper-efficient programming. You know the kind, where people optimize code just to get a meager 0.001% efficiency increase. The kind where people use oddball programming logic and global variables galore just so that the executable takes up slightly less memory. Etc.

It's like I always said: biology is just like code. The problem with the analogy people normally take with code is they think of biology as GOOD code. Code that is modular (not spaghetti), readable (has good names) and basically understandable (simple elegant logic). But what this analogy misses is that the more stupid and incompetent the programmer the less these things hold until at one point it is no longer worth it to fix bugs in code instead of scrapping it altogether and starting from scratch. The thing with biology is that the programmer is natural selection. A blind agent with absolutely no intelligence whatsoever. Any code written by natural selection, almost by definition, has to be horrible. And this is exactly what we're finding.

How ridiculous , and how arrogant of humans to think this way , just because you can't understand something , doesn't mean it is badly designed , it just means you haven't uncovered everything in the universe , it means you have a long list of things and events to try to understand , we didn't even understand the atom , let alone the bacteria , our understanding of a simple atom is a merely pathetic 20% at best , we can't even give a single reason why electrons don't free fall into the positive nucleus , we can't explain gravity , quarks , photons and so many others , all we managed to do is conceive mathematical abstractions to describe some of their functions , not their entities .

If their is one thing that my study of genetics and human body has taught me , is that bio systems are incredibly efficient , thorough and organized , otherwise the possibiliy of you moving a single muscle will so minuscule you wouldn't even exist , the human heart is the most efficient machine in the whole universe even more efficient than stars , things like these are never the outcome of a random or blind construct , but an invincible and incredibly amazing one .

I really like this interpretation. You could even go crazier and say it's like we are taking already compiled binaries and trying to extract out its subparts and divine all possible interactions. The catch is we have no model of the machine which executes that code, and it has taken decades to create systems that, with much effort, can read and write the code.

I wonder whether we have our metaphors entirely wrong. We alway try to compare our understanding of ourselves to the most complicated machines we have managed to invent so far. Descartes had his analogy of a mechanical duck, maybe computer is our mechanical duck - no more illustrative of the problem. The thing is that we have no machines that reproduce themselves or function without so sort of a central organization. The program may be as poor an analogy of what goes on than anything else.

As to where the information is in biology - I find that an interesting question as well. I some ways organism is also like a history book: it stores responses to past events in its structure and functions as well - but not as a simple record of the events. I believe evolutionary biology works on this premise. Also maybe the information is not in the DNA or the cellular parts but in their interactions. Over time, however, if you change the DNA it can reprogram its cellular surroundings but not in easily predictable ways.

I think that modeling networks is important but so much is unknown about biology. There are still some low-hanging fruit to be found: one can gain some insights from correlative analysis of various data type. This seems to be true at least of cancer biology, although drug development in this domain has been difficult as well. One does not necessarily have to understand a system completely in order to kill it - something we know all too well from other domains.

How ridiculous , and how arrogant of humans to think this way , just because you can't understand something , doesn't mean it is badly designed , it just means you haven't uncovered everything in the universe , it means you have a long list of things and events to try to understand , we didn't even understand the atom , let alone the bacteria , our understanding of a simple atom is a merely pathetic 20% at best , we can't even give a single reason why electrons don't free fall into the positive nucleus , we can't explain gravity , quarks , photons and so many others , all we managed to do is conceive mathematical abstractions to describe some of their functions , not their entities .

If their is one thing that my study of genetics and human body has taught me , is that bio systems are incredibly efficient , thorough and organized , otherwise the possibiliy of you moving a single muscle will so minuscule you wouldn't even exist , the human heart is the most efficient machine in the whole universe even more efficient than stars , things like these are never the outcome of a random or blind construct , but an invincible and incredibly amazing one .

Well, see I think the problem is your use of the word "designed."

You look at the final product and say "gee, by golly, that looks mighty well designed."

In reality, the process that built the final product took millions of years of random mishmash resulting in trillions of failed experiments (that would, likely, by your own measure NOT be considered "good design"... for instance: Gastrochisis, Sirenomelia, Craniothoracopagus, Acardius, Anencephaly... etc) I'm simply listing fetal development defects since those have the largest visceral impact for my point. But the fact is, that the final product HAPPENS to work, at the cost of literally trillions of other attempts that DID NOT WORK.

Is trial and error "good design?" Because that's all that it is. Just because these things are so small and so complex that it makes it very difficult for us to unravel, and just because the heart works REALLY well for what it does (lets just ignore the 1,000,000,000,000,000 other attempts at hearts that have failed over the past 4.5 billion years) does that mean that it is "designed well." (And I would love for you to qualify the statement about hearts being more efficient than stars... on what scale or measure are you using to come up with that?)

Even WE acknowledge that trial and error does not result in good design. For instance.. lets take our attempts at Flight. The wright brothers flew by trial and error. Many many failed attempts to build a plane. When they finally did get a working plane, the world over gasped! What GOOD DESIGN! They have built something that allows man to fly!

But looking back, we KNOW that it was NOT good design. It was very poor design that happened to work through a process of trial and error. Now that we understand the fundamentals of aerodynamics we can ACTUALLY methodically DESIGN things, design them in such a way that we don't need trial and error, we look at the end desire, and start modeling.

There is no "end goal" for evolution... it just methodically tries and fails over and over and over. If some random bit of crap happens to function really well, regardless of how entangled of a mess it causes to reverse engineer, it is integrated into the system.

We KNOW that trial and error does not result in "good design." It can produce things that WORK... you betcha, but it requires a whole lot of failing to get there, and the final result, while it "happens" to get the job DONE, does not mean that it is the "best" possible solution for the job.

I don't think anyone is claiming that we know everything, but it is VERY easy to recognize bad design that works when you see it. And you made the statement "how arrogant of humans" as if... there is something ELSE that could be "arrogant" that we know of, implies that you're one of those creationists that scoffs at science. I have very little respect for people who believe in fairies.

Given the incredible amount of bugs (diseases) and redundancy found in the human system, it can't be anything else but a gruesome bunch of copy/pasted spaghetti code.

I always understood DNA to be the code that runs the body too, but maybe it's the other way around, maybe DNA is the interpreter, and we need to figure out what the code is. Also, the code is likely not linear. Maybe our insistence of finding a way to reduce cells to turing machines is what holds back our understanding.

Then again, what do I know, I'm just a software developer. What I do know is that sometimes in software by passing through the hands of enough bad programmers, a piece of code can "evolve" unintended but desirable behavior. I've come across more than one bug report where someone lamented a feature, never consciously designed, that disappeared because the code was cleaned up to do what it was supposed to.

I've read a lot on this topic years ago, this article was a nice and easy to read catch-up. Thank you.

I was surprised by the almost helpless tone given by many of the leaders in this field. This is a relatively young field, and while physicists need to mathematically reconcile 10's or 100's of particles, systems biologists must reconcile 1000's of genes and millions of chemical entities. Someone will eventually find that anchor point they are looking for. As another poster mentioned, it seems logical there should be one given this is an evolved system with a lot of redundancy.

Articles on quantum physics and this one here make me wonder how soon we will reach the limit of our own brain to comprehend the new information we are uncovering. At some point it will be like a dog staring at a circuit board.

Sure, if you assume that every CPU is custom built and no two transistors have the same properties.

That, basically.Programmers are probably pretty good with code, but I want most of them a good distance away from genetics experiments, thank you very much. Just seeing what computer science calls a neural network makes me shudder at the thought that, one day, some computer scientist thought this simulated anything remotely close to even a single neuron...And at this point, we will likely reach the limits of our computers faster than that of our brains, imo.

Sure, if you assume that every CPU is custom built and no two transistors have the same properties.

That, basically.Programmers are probably pretty good with code, but I want most of them a good distance away from genetics experiments, thank you very much. Just seeing what computer science calls a neural network makes me shudder at the thought that, one day, some computer scientist thought this simulated anything remotely close to even a single neuron...And at this point, we will likely reach the limits of our computers faster than that of our brains, imo.

If by behind the times you mean "able to recognize overblown bullshit when it's claimed" yes, I am.This experiment was cat scale, it didn't simulate anything close to a cat's brain. Or, even, an ant. Hell, without immense shortcuts, we can't do a full cell because there's still too much unknown, I shudder to think the shortcuts involved into claims of recreating a brain.

Sure, if you assume that every CPU is custom built and no two transistors have the same properties.

Actually, wouldn't it be more like a heterogeneous system of n CPUs, each of which is custom built and executes the parts of the "code" that apply to it from the "global code cache"? And as well as having multiple CPUs of different architectures, some of the "features" of the system are distributed "programs" requiring 2 or more of these heterogeneous CPUs, and the part that runs on CPU1 is written in a different language than the part that runs on CPU 2?

And then you need to know the correct power-on sequence so that everything actually works? You could leave out the odd CPU here or there if you're willing to live (pun intended) without certain features, but some of the systems are so interconnected, if you miss one CPU, nothing works.

And CPU X in unit 1 (person 1) might be a slightly different model than CPU X in unit 2 (person 2), maybe like Pentium with FDIV bug, Pentium without FDIV bug, meaning that you SHOULD get similar behavior in persons 1 and 2 from the same input, but the odd time, it's not quite the same..

Given the incredible amount of bugs (diseases) and redundancy found in the human system, it can't be anything else but a gruesome bunch of copy/pasted spaghetti code.

I always understood DNA to be the code that runs the body too, but maybe it's the other way around, maybe DNA is the interpreter, and we need to figure out what the code is. Also, the code is likely not linear. Maybe our insistence of finding a way to reduce cells to turing machines is what holds back our understanding.

Then again, what do I know, I'm just a software developer. What I do know is that sometimes in software by passing through the hands of enough bad programmers, a piece of code can "evolve" unintended but desirable behavior. I've come across more than one bug report where someone lamented a feature, never consciously designed, that disappeared because the code was cleaned up to do what it was supposed to.

One common misconception here is that DNA is the only code the body reads. Not so. It's the base code, which everything else is derived from. But that everything else consists of regulatory proteins, various varieties of interfering RNAs, RNAs which can form regulatory structures of their own, etc etc. You're basically running into one of the problems stated in the article " that the relevant program logic should actually be found in the DNA sequences". It is not the DNA itself, but the interaction of all the bits it produces that controls the organism.

Since that behaviour is true it's not really like a computer program at all.

It's like I always said: biology is just like code. The problem with the analogy people normally take with code is they think of biology as GOOD code. Code that is modular (not spaghetti), readable (has good names) and basically understandable (simple elegant logic). But what this analogy misses is that the more stupid and incompetent the programmer the less these things hold until at one point it is no longer worth it to fix bugs in code instead of scrapping it altogether and starting from scratch. The thing with biology is that the programmer is natural selection. A blind agent with absolutely no intelligence whatsoever. Any code written by natural selection, almost by definition, has to be horrible. And this is exactly what we're finding.

How ridiculous , and how arrogant of humans to think this way , just because you can't understand something , doesn't mean it is badly designed , it just means you haven't uncovered everything in the universe , it means you have a long list of things and events to try to understand , we didn't even understand the atom , let alone the bacteria , our understanding of a simple atom is a merely pathetic 20% at best , we can't even give a single reason why electrons don't free fall into the positive nucleus , we can't explain gravity , quarks , photons and so many others , all we managed to do is conceive mathematical abstractions to describe some of their functions , not their entities .

If their is one thing that my study of genetics and human body has taught me , is that bio systems are incredibly efficient , thorough and organized , otherwise the possibiliy of you moving a single muscle will so minuscule you wouldn't even exist , the human heart is the most efficient machine in the whole universe even more efficient than stars , things like these are never the outcome of a random or blind construct , but an invincible and incredibly amazing one .

Well, see I think the problem is your use of the word "designed."

You look at the final product and say "gee, by golly, that looks mighty well designed."

In reality, the process that built the final product took millions of years of random mishmash resulting in trillions of failed experiments (that would, likely, by your own measure NOT be considered "good design"... for instance: Gastrochisis, Sirenomelia, Craniothoracopagus, Acardius, Anencephaly... etc) I'm simply listing fetal development defects since those have the largest visceral impact for my point. But the fact is, that the final product HAPPENS to work, at the cost of literally trillions of other attempts that DID NOT WORK.

You should think of it in terms of did not not work rather than work. When something works there is usually intention behind it, not so with evolution. I know you know this but sometimes the language can be misleading.

Ars takes an in-depth look at the field of synthetic biology—the computer modeling of cells and biological systems—as it retreats from the complexity it had hoped to avoid. Can it stop?

Read the whole story

Ray Kurzweil should read this article (see /.).

Anyhow, part of the problem is that folks are bringing a physics or engineering mindset to the problem. Physics and engineering are useful and have a lot of useful tools but trying to impose the patterns we find there is likely to be disappointing. The problem is that we don't know what is significant in biology. We know in physics that if we measure a, b, c qualities at state 0 we should be able to predict the values of those qualities at state 1 and we care about those predictions because of our own interests (e.g. building a bridge). What do cells care about? What matters to a cell? You might say nothing because it's not human but you'd be missing the larger structural point. You have to assume a goal in order to make sense of all of that data and once you can make sense of it then you can start making and testing theories. So, what a cell's goal? Primarily its goal is not dying (not dying is different than living). Secondarily replicating before dying. Or maybe replicating is primary and not dying is secondary? Anyhow there's a organizing principle.

The article was absolutely excellent, and the discussion has been quite thought provoking as well. While the tone may seem pessimistic at first, I find it inspiring that so much is yet to be done before we even approach "understanding" of an organism as a whole. Many lifetimes worth of discoveries ahead to be uncovered! A cell's "primary goal" is an interesting idea to ponder... especially in the case of multicellular organisms. But it maybe anthropomorphizes the cell a bit too far; I'd prefer to say "function". And the cell's ultimate function may change depending on the context. Although these functions have obviously been a subject of intense research, we are still discovering new functions for various cells (at least in my field, immunology). How to put all these multiple context-dependent functions in multiple, interacting cell types together in a unified model with predictive power remains to be seen. Perhaps the only hope is to collaboratively and painstaking build every single feature into a model that is itself to complex for one person to fathom in its entirety, but may at prove useful, both in end result and the knowledge gained in the construction process. This sounds like the VPH, so I guess I am aligned with the goal there.However, in terms of making useful medical discoveries or therapeutics, perhaps the traditional "educated guess-->in vitro/animal work-->trial and error in humans, return to educated guessing" approach will be faster.

Paradoxically, the more detailed and physically realistic a simulator becomes, the less it helps a scientist understand the referent, and more likely it is to actually mislead, due to small chaotic effects resulting in higher-level instability and infidelity. A great example is the "Blue Brain" simulator, which boasted it was going to be the most detailed simulation of neurons ever; but they had serious problems with inhibitory dynamics.

I worked with Dr. Randy O'Reilly at the University of Colorado -- he advocates a "mid-level" degree of neural simulation, which captures the essential larger-scale computational dynamics, while eschewing lower-level details or insistence on complete bio-realism. For example, his neural modeling system, called Leabra/Emergent, uses a graded rate-code, not spiking, to approximate the firing response of small groups of neurons. And he uses an algorithmic form of inhibitory dynamics which is inherently very stable and computationally efficient, while still achieving the essential regulatory dynamics observed in neural populations.

These kinds of ideas might be usefully applied to many other forms of simulation, including bio-system simulations, as well as environmental simulation.

If by behind the times you mean "able to recognize overblown bullshit when it's claimed" yes, I am.This experiment was cat scale, it didn't simulate anything close to a cat's brain. Or, even, an ant. Hell, without immense shortcuts, we can't do a full cell because there's still too much unknown, I shudder to think the shortcuts involved into claims of recreating a brain.

While I don't completely disagree with you, I think you're missing the forest for the trees.

For some reason, you think you need to simulate the minutiae to accurately simulate larger phenomenon.

This is actually USUALLY not the case.

If you take a black box that performs some function, and are able to feed it inputs and record every output... do you need to know what the black box is doing to simulate it? Do you need to know how many atoms it is made of or the quantum interactions of the particles inside the black box?

Usually no. If you can emulate it, by making a simulation that produces the exact same outputs based on the same inputs... then what the black box is made of, or how it functions is ABSOLUTELY and COMPLETELY unnecessary.

That's the angle that they're taking with blue brain. I'm not saying that its guaranteed to work, but what I AM saying is that you don't necessarily need to know what is happening on an atomic level inside the neuron if you create a simulation that can accurately mimic all of the possible outputs of a neuron based on various inputs. You don't need to know how to fold proteins and decode DNA to mimic a neuron... similarly we don't need to model every quantum particle in a fluid dynamic system in order to accurately model airflow around a car... similarly I don't need to model every atom when I want to calculate the trajectory of a satellite.

They certainly DID accomplish a lot more than an ant's nervous system inside a computer. In fact, I think they're doing far more than an ant on MANY different fronts: http://www.youtube.com/watch?v=nUQsRPJ1dYw This little robot still blows my mind.

You want them to simulate the ant on an atomic level? Is that what you're asking for? WHY? What purpose does that serve? If you're a biologist and want to understand DNA, that is a COMPLETELY different problem on a COMPLETELY different scale. I think your scoffing at computer science is unfortunate and myopic. Scientists should not scoff at each other. There are many ways to tackle our problems, to answer the questions that remain, and frankly, it will take everything we've got to answer some of these questions... because they certainly are not easy... from what is "sentience" to the quantum world "does higgs boson exist?" to "how the hell does DNA work" to "what was before the big bang" we've got a lot of work to do.

I just registered a profile here in Ars to congratulate Robert on an excellent article. I am a researcher associated with systems and synthetic biology and I thorougly enjoyed this poignant analysis of two promising fields that have been getting a reality check in the last few years.